System and method for detecting signals across radar and communications bands

ABSTRACT

Apparatus and methods for processing RF signals are disclosed. A method according to the invention includes receiving a set of time domain energy samples representing signal energy present in an RF spectrum, transforming the set of time domain energy samples into a set of frequency domain power samples, determining from the set of frequency domain power samples whether a signal of interest is present in the RF spectrum, and forwarding to a follow on system a subset of the set of frequency domain power samples, wherein the subset corresponds to the signal of interest. Transforming the time domain samples can include dividing the set of time domain energy samples into a plurality of N windows, each of which is associated with a predefined window period, and performing an FFT on each said window to generate a set of K frequency bins, wherein each frequency bin has a value based on energy present in a predefined frequency band during the corresponding window period. Determining whether the signal is present can include generating an energy map that represents energy present in the RF spectrum as a function of frequency and time. The energy map can be a bitmap having N×K frequency cells, wherein each frequency cell has a binary value based on the value of a corresponding frequency bin. The binary value can be based on whether the value of the corresponding frequency bin exceeds a predefined threshold.

RELATED APPLICATIONS

This application claims priority from U.S. Provisional PatentApplication Ser. No. 60/111,558, filed Dec. 9, 1998, the contents ofwhich are hereby incorporated by reference. This application claimspriority from U.S. Provisional Patent Application Ser. No. 60/111,560,filed Dec. 9, 1998, the contents of which are hereby incorporated byreference. The subject matter disclosed herein is related to the subjectmatter disclosed in copending application Ser. No. 09/456,584, filedDec. 8, 1999, entitled “System and Method for Limiting Histograms.” ThisApplication is a continuation of application Ser. No. 09/456,726, filedDec. 8, 1999 now U.S. Pat. No. 6,191,727.

FIELD OF THE INVENTION

The present invention relates in general to radio frequency (RF) energyanalysis systems and methods. More particularly, the present inventionanalyzes wideband RF in realtime to extract potential signals ofinterest while removing noise. The present invention performs theextraction and analysis function in all bands, Hf to microwave, forradar and communications signals.

BACKGROUND OF THE INVENTION

Typical signal collection and processing systems detect the presence ofsignals of interest in an RF environment by determining whether signalpower within a certain frequency range exceeds a predefined thresholdlevel for a sufficient duration of time. “Channelized” systems, forexample, tune a receiver having a known bandwidth to a certain frequencyand collect all the RF energy present in the environment in thatfrequency range. These systems determine whether the signal powerexceeds the predefined threshold and, if so, conclude that a pulseexists in that range. The channelizers then define a pulse start time asthe time at which the signal energy first exceeded threshold, and apulse end time as the time that signal energy falls below threshold. Aknown deficiency of such channelized systems is that they requiresignificant resources to monitor a large number of frequency bands.Another deficiency of these systems is that each channelizer is tuned toa fixed bandwidth that may or may not be consistent with the bandwidthsof the signals of interest. Consequently, these systems do not provideoptimal sensitivity.

“Compressive receivers” continually sweep a broad bandwidth with anarrowband filter. These systems can detect narrowband pulses in abroadband environment, but suffer from an inability to detect thepresence of signal energy that is present in the environment duringperiods in which the narrowband filter is not tuned to the frequencyband in which that signal energy is present. Additionally, the bandwidthof the narrowband filter is tuned to a fixed bandwidth that may or maynot be consistent with the bandwidths of the signals of interest.Consequently, these systems do not provide optimal sensitivity, do notnecessarily capture the signal of interest, and are analog systems.

Instantaneous Frequency Measurement (IFM) receivers minimize the sweeptime limitations of the compressive receiver by providing a broadbandfrequency discriminator that rapidly responds to a signal's presence.The IFM receiver, however, is unable to provide accurate frequencymeasurements in the presence of multiple simultaneous input pulses, asare typically encountered in crowded RF environments.

Broadband signal processing systems are often required to detect thepresence of narrowband signal energy in a wideband RF environment thatincludes, for example, radar pulses and/or communications pulses. It isdesirable that such systems are able to detect all that RF energy thatis present in a wide frequency range for a certain period of time. It isalso desirable to minimize the resources required to detect thesesignals. Designers of such signal processing systems, therefore, wouldbenefit from methods and apparatus that analyze wideband radio frequencyspectra that include both radar and communications signals to extractpotential signals of interest while removing noise and other unwanted RFenergy.

SUMMARY OF THE INVENTION

The present invention satisfies these needs in the art by providingapparatus and methods for processing RF signals. The inventive methodcomprises generating an energy map of collected radio frequency (RF)energy as a function of time and frequency for a predefined dwell periodand dwell bandwidth. The collected RF energy can include energy fromcommunications signals as well as radar signals, transient signals aswell as continuous signals. From the energy map, it can be determinedwhether a pulse is present in the RF spectrum. If a pulse is present inthe RF spectrum, then a pulse bandwidth and pulse duration can bedetermined from the energy map.

The energy map can be generated by dividing the dwell period into a setof k time windows and dividing the dwell bandwidth into a set of nfrequency bins. An energy grid comprising n×k frequency-time cells canthen be generated. Each frequency-time cell corresponds to one of thefrequency bins and to one of the time windows and has a value based onthe collected RF energy present in the corresponding frequency binduring the corresponding time window. A binary value can be assigned toeach of the frequency-time cells based on whether the collected RFenergy present in the corresponding frequency bin during thecorresponding time window exceeds a predefined energy presencethreshold. If noise is present in the RF spectrum, the noise can befiltered from the energy map.

If a pulse is present in the RF spectrum, a tag can be generated for thepulse that includes a pulse characterization parameter thatcharacterizes the pulse. The pulse characterization parameter can bebased, for example, on pulse width, center frequency, angle of arrival,or time of arrival.

A method according to the present invention can also include “pulsehealing.” That is, for a first pulse and a second pulse, a combinedpulse duration can be defined that extends from a start time of thefirst pulse to an end time of the second pulse. It is then determinedwhether the start time of the second pulse exceeds the end time of thefirst pulse by less than a predefined threshold, which can be based, forexample, on the combined pulse duration. If the start time of the secondpulse exceeds the end time of the first pulse by less than thepredefined threshold, then the first pulse and the second pulse arecombined into a single pulse (i.e., the single pulse is “healed”).

Similarly, pulses can be “healed” in frequency. That is, for a firstpulse and a second pulse, a combined pulse bandwidth can be defined thatextends from a lower frequency of the first pulse to an upper frequencyof the second pulse. It is then determined whether the lower frequencyof the second pulse exceeds the upper frequency of the first pulse byless than a predefined threshold, which can be based, for example, onthe combined pulse bandwidth. If the lower frequency of the second pulseexceeds the upper frequency of the first pulse by less than thepredefined threshold, then the first pulse and the second pulse arecombined into a single pulse.

Another method for processing RF signals according to the inventioncomprises receiving a set of time domain energy samples representingsignal energy present in an RF spectrum. The set of time domain energysamples can be transformed into a set of frequency domain power samples.Transforming the set of time domain samples into a set of frequencydomain samples can include dividing the set of time domain energysamples into a plurality of N windows, each of which is associated witha predefined window period. For each of the N windows, an FFT isperformed to generate a set of K frequency bins. Each of the frequencybins has a value based on energy present in a predefined frequency bandduring the corresponding window period.

From the set of frequency domain power samples, it can be determinedwhether a signal of interest is present in the RF spectrum. A subset ofthe set of frequency domain power samples can be forwarded to a followon system, where the subset corresponds to the signal of interest. Todetermine whether a signal of interest is present can include generatingan energy map that represents energy present in the RF spectrum as afunction of frequency and time. The energy map can be a bitmapcomprising N×K frequency cells, wherein each frequency cell has a binaryvalue based on the value of a corresponding frequency bin. The binaryvalue can be based, for example, on whether the value of thecorresponding frequency bin exceeds a predefined threshold.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing summary, as well as the following detailed description ofthe preferred embodiments, is better understood when read in conjunctionwith the appended drawings. For the purpose of illustrating theinvention, there is shown in the drawings an embodiment that ispresently preferred, it being understood, however, that the invention isnot limited to the specific apparatus and methods disclosed.

FIG. 1 is a plot of RF energy as a function of frequency and time.

FIG. 2 is a block diagram of an RF energy collection and analysissystem.

FIG. 3 is a block diagram of an RF energy mapper according to thepresent invention.

FIG. 4 is a block diagram of a tag generator according to the presentinvention.

FIG. 5 is an RF energy bitmap according to the present invention.

FIG. 6 is a block diagram of a tag screening process according to thepresent invention.

FIG. 7 is a block diagram of a histogramming limiter according to thepresent invention.

FIG. 8 provides a frequency histogram with a pulse duration histogramfor one frequency bin.

FIG. 9 provides a flowchart for a frequency histogramming limiteraccording to the present invention.

FIG. 10 provides a flowchart for a duration histogramming limiteraccording to the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Definitions

FIG. 1 provides a general reference for definitions of certain termsthat will be used throughout this disclosure. A “dwell” is a collectionof radio frequency (RF) spectra within a lower frequency limit, f₁, andan upper frequency limit, f_(u) with a center frequency f₀ halfwaybetween f₁ and f_(u), during period t_(s) to t_(e). An “emitter” is anRF source that contributes to the spectra. A “dwell data set” is aformatted data set representative of all the spectra contained in adwell.

A dwell results from a receiver being tuned to f₀ and the RF energybeing collected over a period between a dwell start time, t_(s), and adwell end time, t_(e). For events within a dwell, time is relative,where the beginning of a spectrum observation period, i.e., the dwell,is zero time and t₁ and t₂ are some number of ticks on a counter that isinitialized at the beginning of the spectrum observation period.

A “pulse” is an energy burst occurring within a dwell. Typically, manypulses occur within one dwell. A pulse is characterized by an upper andlower frequency bound, f₂ and f₁, respectively, and occurring betweenthe start of the energy burst, t₁, and the end of the energy burst, t₂.A pulse has a center frequency of f_(c) halfway between f₁ and f₂, and a“pulse duration” of t₂−t₁. A “tag” is a characterization of a pulse andcontains the value t₁, t₂, f₁, and f₂, which mark the pulse boundaries.A “tag generator” is a device that converts a pulse into a tag. A“frequency segment” consists of the frequencies spanned by a frequencybin (i.e., an FFT bin).

RF Energy Mapper

FIG. 2 is a block diagram of an RF energy collection and analysis systemaccording to the present invention. A wideband receiver 10 receivesanalog RF signals via an antenna 12. Receiver 10 passes the analogsignals through an analog-to-digital (A/D) converter 14, wherein theanalog signals are converted to digital signal samples via well-knownanalog-to-digital conversion techniques. A/D converter 14 outputs adigitized spectrum in the time domain, that is, a stream of digitalsignal samples that represents the received signal energy as a functionof time.

According to the present invention, the stream of time domain samples isinput into an RF energy mapper 100. RF energy mapper 100, which isdescribed in detail below, performs a spectral analysis on the inputsignal samples to detect the presence of signals of interest in thedigitized spectrum. Energy tags are generated for the signals ofinterest and can be passed on to one or more follow-on systems forfurther analysis. Preferably, RF energy mapper 100 stores the signalsamples and can forward the stored signal samples to a follow-on systemon request.

Generally, RF energy mapper 100 provides apparatus and methods fordetecting and capturing broadcast radar and communications signals thatare present in a frequency spectrum having a spectrum bandwidth that iswide relative to the bandwidth of the signals. RF energy mapper 100detects and captures both short and transient signals (e.g., frequencyhoppers), as well as conventional continuous (i.e., CW) signals (e.g.,air guidance signals) and continuous modulated signals (e.g., commercialbroadcast).

The input into RF energy mapper 100 is a stream of digitally encodedsignal samples sourced by a wideband receiver. The bandwidth of thewideband receiver typically encompasses many hundreds or thousands ofsimultaneously transmitted signals. The system outputs tags for thosecollected signals. The tags describe the start and stop time and thelower and upper frequency bound of all of the signals meeting presetcriteria for tagging. Where a signal is continuous rather thantransient, such signal will be noted in the output tag as having a timeperiod longer than the criteria for transient signals. A second productof the system is a randomly accessible delay line that stores all FFTrepresentations of the incoming spectrum so that the signal associatedwith the tags is also outputted. The follow-on system uses the tags torequest the signal samples associated with the tag.

An advantage of the system is that any and all signals within thespectrum being intercepted by the wideband receiver can be captured anddescribed for downstream (i.e., follow on) systems that will furtherprocess the signals. Thus, RF energy mapper 100 provides a veryefficient method for providing the comparatively narrowband signalsalong with their descriptors (i.e., the so-called tags). Preferably, RFenergy mapper 100 tags and stores both communications signals and radarsignals.

As shown in FIG. 3, RF energy mapper 100 receives digital receiversamples in the time domain from one or more digital receivers, andgenerates selected energy tags for a follow-on subsystem. Preferably, RFenergy mapper 100 performs this function in a two-step process. First, atag generation process 102 identifies those spectral energy segments inthe RF search band that are above a minimum amplitude and are not noiserelated. Tag generation process 102 is illustrated in FIG. 4. Next, atag screening process 104 is employed to limit the number of tags thatare output to the follow-on system. Tag screening process 104 isillustrated in FIG. 6.

As shown in FIG. 4, a tag generator 102 according to the present caninclude an FFT and windowing function 106, a thresholding function 108,a pattern recognition and noise filter function 110, a line of bearing(LOB) filter 112, a signal of interest (SOI) energy definition function114, and a signal storage function 116.

FFT and windowing function 106 serves to convert the time domainrepresentation of the spectrum into its equivalent in the frequencydomain. FFTs are performed on the signal samples at rates thataccommodate the signal set intended to be captured. Thus, FFT/windowingfunction 106 is typically constructed to provide for variable frequencybinning and variable FFT rates. For communication systems intercept, forexample, narrow frequency bins can be used as part of the FFT, while forradar intercept, wide frequency bins with FFTs executed at a much morerapid rate is required. FFT bin size selection will determine thedetectability of the signal, as well as the system's ability to measurethe arrival and departure time of the signals to be intercepted.

The output of the FFTs, which is a set of frequency domain powersamples, is stored in signal storage 116. Each of the frequency domainpower samples has a value based on the RF energy that is present in thedwell bandwidth (f_(u)−f₁) during the corresponding FFT window. Thefrequency domain samples are stored until a decision is made as towhether a pulse of interest is present in the RF spectrum. As will bedescribed in detail below, if a pulse is detected in the RF spectrum,the tags that correspond to that pulse are forwarded to a follow-onsystem for further processing. Since the frequency domain samples arestored in signal storage 116, the frequency domain samples can beforwarded to the follow on system on request. The follow on system canthen perform an inverse FFT on the requested frequency domain samples,which will be, in general, a subset of the set of frequency domainsamples stored in signal storage 116, to reconstitute the signal ofinterest in the time domain.

It is important to note that this approach (i.e., storing and forwardingfrequency domain samples) is much more efficient than storing andforwarding the corresponding time domain samples. According to theinvention, only those bins that are required to reconstitute arelatively narrow band signal detected in a relatively wide bandspectrum need to be forwarded to the follow on system. At the same time,no information is lost because the set of frequency domain samplesincludes all the signal information that the time domain samplesinclude.

Thus, as a practical consideration, storing the frequency domain samplesin signal storage 116 (which is basically a delay line) provides for anefficiency of processing of the selected signals. A broadband receiverthat captures all the signals within its bandwidth makes it moredifficult to process the multiplicity of individual narrowband signals.Preferably, the receiver is matched to the bandwidth of the signaldesired to be processed. In such an implementation, with the signalsbeing stored as their frequency domain representation, the follow onsystems need only process a very small part of the entire interceptedspectrum related to the (comparatively) narrow band. This can result inan order of magnitude decrease in the follow-on processing of thesignal, the order of magnitude being determined by the ratio of the fullspectrum to the signal bandwidth.

The output of the FFTs is also inputted to threshold detector 108, whichbasically converts the 3-dimensional output of the FFT function into a2-dimensional representation. More specifically, the signal as presentedat the output of the FFT function is a series of FFT bins. Thus, thereis a first dimension, i.e., a representation of the spectrum infrequency. Second, the FFTs are performed periodically (i.e., once eachFFT window period), thus there is a time dimension. Third, the value theFFT assigns to each frequency bin is a power level that represents thesignal energy in that frequency bin during that window period. Thus, thethird dimension is signal power.

For each frequency cell for each FFT window period, a binary decision ismade to indicate the presence or absence of energy relative to a noisefloor computation that, preferably, is continually adjusted for the RFintercept environment. The frequency-time-power vectors that areinputted to thresholding function 108 is reduced to a frequency-timegrid, whose entries have a binary value (i.e., either a 1 or a 0) thatdepends on the power within each cell. The thresholding is a decisionthat can be made using varying degrees of complexity. The simplest formis a fixed level entered into the thresholder and any bin having a powerlevel that exceeds the threshold results in the power level beingconverted to a one; wherever it is below the threshold, the power levelis replaced by a zero. Thus, the output of threshold detector 108 is agrid in frequency and time that depicts significant power exceedances ineach of the bins.

An exemplary frequency-time grid is shown in FIG. 5 (where Xs are usedto represent bins having a value of one, and blanks are used torepresent bins having a value of zero). As shown in FIG. 5, a preferredfrequency-time grid includes 1024 frequency cells for each 2.56microsecond window period. It should be understood that the number offrequency cells (i.e., FFT bins) can be selected based on therequirements of the specific application. For example, some radarsignals are known to have pulse widths as low as 50 nanoseconds with aduty cycle on the order of 1%, while other radar signals can have pulsewidths up to 1.5 microseconds at nearly 50% duty cycle. Theproliferation of radar signals is typically in the 2-18 GHz band, butcan occur in the range of 500 MHz to 40 GHz. Communications signals, onthe other hand, can be much more narrow band than radar signals, andtypically have nearly 100% duty cycle. It is known, however, that forsignal systems such as frequency hoppers, the 100% duty cycle isrelative to a hop frequency. Most communications signals are under 2GHz, but can extend above 2 GHz in microwave and millimeter wavecommunications.

One advantage of the present invention is that the RF energy mapperprovides apparatus and methods for detecting the presence of radarsignals as well as communications signals in the same dwell data set. Asystem according to the present invention can utilize the RF energymapper for both radar and communications intercept, although, in apreferred embodiment, it does not perform them simultaneously, butrather sequentially.

Threshold detector 108 can also be built to include more complexcriteria for inclusion of a 1 or a 0 in each cell. That criteriaanalyzes the degree to which the power exceeds the threshold and for theduration that the power is there. Thus, short signals that barely makethresholds are more likely to be noise than signal, while strong signalsof short duration are more likely to be signals than noise. Signals oflow power with extended duration. are also more likely to be signalsthan noise. Thus, a set of rules are formulated and implemented in acombination of hardware, firmware and software to execute this moreelaborate threshold making function.

At this point, there is an N-to-1 data reduction in the amount of datapassing through the system, where N is determined by the dynamic rangeof the digital samples representing the spectrum. For example, an 8-bitcode would result in an 8-bit bin size, while a 16-bit code would resultin a 16-bit bin size. In the first example, there would be an 8-to-1data reduction, while in the second example there would be a 16-to-1data reduction. The data reduction is a function of the requirements ofthe system that includes the RF Energy Mapper as a subsystem.

The frequency-time grid output from threshold detector 108 is thensubmitted to a noise filter 110, wherein the grid is processed toeliminate noise. Noise, as that term is used herein, means anythingother than a pattern indicative of a signal of interest (SOI), and thatwill be most of the energy in the frequency-time grid. Thus, a systemaccording to the present invention is very much a noise processor. Thenoise filter is used principally to separate pulsed from continuoussignals and to eliminate obvious noise patterns. “Continuous” includesmodulated continuous waveform (CW) which, on a map, will appear as acontinuous ragged signal relative to the frequency cells occupied.Lightning strokes, ignition noise, and other spiking, broadband noiseproduces clear patterns that can be deleted.

Once the noise is eliminated from the signals in the frequency-time bitmap, the bit map is passed to a line of bearing (LOB) filter 112. Lineof bearing is also commonly referred to as “angle of arrival” or“azimuth.” The next level of processing uses angle of arrival to passonly those signals that are radiating from a sector or sectors thatcould contain signals of interest. The angle of arrival data reductionin a uniformly distributed environment will be the ratio of the sectorsize to 360 degrees.

Those spectral energy segments that have not been eliminated as noiseare then subjected to pattern recognition and SOI energy definitionprocess 114 to formally define the energy time and frequency extent.Process 114 follows a set of rules (which can be implemented inhardware, firmware, or software) for drawing rectangles in time andfrequency around the patterns in the frequency-time grid. The length andwidth of those rectangles are measured in frequency and time, withspecific starting and stopping positions, such as a lower frequencyextent, f_(L), an upper frequency extent, f_(U), a start time of therectangle, t₁, and an end time of the rectangle, t₂. The rules are setto encompass the whole frequency-time pattern of a given transient pulsewhen characterizing transient pulses. Thus, maximum variations infrequency determine the frequency extents and maximum extents in timedetermine the time extents. Any signal having a duration (t₂−t₁) thatexceeds a preset time duration, T_(max), is considered to be acontinuous signal.

Process 114 also includes rejecting corrupted pulses and connectingfractured pulses in order to define a rectangular area to completelyencompass each valid pulse. Energy may appear disassociated in afrequency-time grid when, in fact, the energy should be treated as aunified transmission. For example, in communications, voice signals havenumerous breaks. Discrete frequency shifts and data communications willresult in disconnections within the bit map when, in fact, it is asingle, unified transmission.

Breaks in a pulse, if less than a preset percentage, can be ignored. Ina preferred embodiment, this so-called “signal drop time percentage” isset at ⅓ of t₂ minus t₁. It should be noted that this percentage is notcritical to the pattern recognition function. It is merely a judgmentalfactor that can be considered a design variable. This sub-functionwithin pattern recognition function 110, which is sometimes referred toas “pulse healing,” can be implemented in hardware, firmware orsoftware.

Then, as a function of the SOI, rules are applied for examination of notonly each region where there is energy evident, but also in thesurrounding region and an estimate is made of the frequency and timeextents and a rectangle in frequency and time is drawn around energycontaining regions. A tag is generated for each rectangle defining itsbandwidth, center frequency, duration, and time of arrival. These tagsare then subjected to a tag screening process as shown in FIG. 6. Thisscreening begins by subjecting the tags to a bandwidth filter 120 and aduration filter 122. Signals that are too short or too long to fit theSOI characteristic will be filtered out Signals with bandwidths notmatching the SOI will also be filtered out. At this point, a refinedexamination of the RF energy map has been made.

For high rate signals, histogramming limiters 130, which are describedin greater detail below, can be used to limit pulses entering thenarrowband processing section, which can be as high as 300,000/pps for apulse doppler emitter. The azimuth and frequency histogrammers 124, 126serve to limit the maximum number of pulses accepted from a singleemitter. In the case of a pulse doppler where a 100 ms dwell is employedduring the intercept, as many as 30,000 pulses could be submitted to thesystem, it is unnecessary and undesirable to collect and process all ofthese pulses. The azimuth and frequency histogrammers will limit pulsesto a programmable maximum, usually 128 pulses in any azimuth frequencyrange (nominally 1.25 MHz by 3 degrees). Typically, 128 pulses will bemore than sufficient to characterize an emitter and track it accurately.In the example provided, a 300:1 reduction with no loss of performanceis realized. For pulse rates under 1 kilopulse/sec with system setupdescribed, no pulses would be lost due to thresholding.

Histogramming Limiters

A histogramming limiter according to the present invention is a systemthat uses histograms to limit data flow through a signal processingsystem so that the system is not overloaded. In a preferred embodiment,the histogramming limiter selectively limits data flow based on densityof signal frequency and signal duration.

As described above in connection with FIG. 2, data flows through thesystem in dwell data sets. Typically, the dwell data set has redundantinformation in the case of high rate emitters when the purpose of thesystem is to detect and locate an emitter. To reduce data flow, ahistogramming limiter allows only essential data to pass through thesystem while blocking redundant data

FIG. 7 provides a flowchart of a histogramming limiter according to thepresent invention. A set of tags is produced, for example, by a taggenerator such as described above. Each tag represents a pulse definedby a start time, end time, upper frequency, and lower frequency. Fromthese values, pulse center frequency and pulse duration can bedetermined. The set of tags is provided as input to the histogramminglimiter.

At step 202, a frequency histogrammer generates a frequency histogramthat represents the number of pulses that fall into each of a pluralityof frequency bins. The frequency histogram is generated based on thecenter frequencies that are included in the tags. At step 204, afrequency limiter determines, for each frequency bin, whether the numberof pulses that fall into that bin exceed a predefined threshold. If so,only the threshold number of tags is passed on further into the signalprocessing system. Thus, the frequency histogrammer limits the number oftags that are allowed through the system based on frequency.

At step 206, a pulse duration histogrammer generates a pulse durationhistogram that represents the number of pulses that fall into each of aplurality of pulse duration bins. The pulse duration histogram isgenerated based on the pulse durations that are included in the tags. Atstep 208, a pulse duration limiter determines, for each pulse durationbin, whether the number of pulses that fall into that bin exceed apredefined threshold. If so, only the threshold number of tags is passedon further into the signal processing system. Thus, the pulse durationhistogrammer limits the number of tags that are allowed through thesystem based on pulse duration.

FIG. 8 provides a frequency histogram example with a pulse durationhistogram for one frequency bin.

FIG. 9 provides a detailed description of a frequency histogrammeraccording to the present invention. As described above, the frequencyhistogrammer receives as input all the tags for a given dwell. In apreferred embodiment, the histogram is empty at the start of each dwellbecause the histogram counts pulses within each dwell period. At step212, the center frequency, f_(c), of each tag is computed by taking anaverage of the sum of f₁ and f₂, the lower and upper frequencies of thetags. At step 214, a histogram bin address is determined for each pulse.The frequency bin within which the pulse falls is determined by f_(c)and its relation to f₁, the lower frequency limit of the dwell. Thus,each frequency bin covers a range of frequencies.

Preferably, the pulse center frequency, f_(c), is rounded to the numericprecision of bin size at step 216. For rapid computation and ease ofconstruction, binary integer bin sizes can be used. At step 218, theresulting binary integer is used as a relative address into thehistogram, which, in a preferred embodiment, is a set of counters inmemory. The content of a counter is advanced by one each time a pulsefalls within its range. When a counter is advanced, the count iscompared to a threshold value, at step 220, and if the count exceeds thethreshold, the limiter stops the tag from proceeding further through thesystem. Otherwise, the tag is passed through the system. The thresholdis a value typically established when the system is initialized.

A similar process occurs for the pulse duration histogramming limiter,as shown in FIG. 10. The input to this part of the histogrammer limiteris the subset of tags output from the frequency histogrammer limiter,and comprises the tag and the frequency bin number associated with eachtag. The first operation is the computation of the pulse duration,t₂−t₁, at step 232. The bin associated with the duration is determined,at step 234, by rounding to a binary integer of length log₂(M), where Mis the total number of duration bins. Preferably, the pulse durationhistogrammer limiter uses a set of histograms, one histogram for eachfrequency bin. At step 234, the frequency bin number in the input thenis used to select the corresponding duration histogram and, at step 236,the pulse duration counter corresponding to the pulse duration bin isadvanced within the selected histogram. The bin count in the selectedbin is then output at step 238. The output bin count is compared to athreshold at step 240 and, if the count does not exceed threshold, thetag is outputted for additional system processing. Otherwise, the tag issuppressed. Again, this threshold is normally set at systeminitialization.

Thus there have been described systems and methods for detecting signalsacross radar and communications bands. Those skilled in the art willappreciate that numerous changes and modifications can be made to thepreferred embodiments of the invention and that such changes andmodifications can be made without departing from the spirit of theinvention. It is therefore intended that the appended claims cover allsuch equivalent variations as fall within the true spirit and scope ofthe invention.

We claim:
 1. A method for processing RF signals comprising: generating awideband energy map of collected radio frequency (RF) energy as afunction of time and frequency for a predefined dwell period and dwellbandwidth; determining from the energy map whether a narrowband pulse ispresent in an RF spectrum defined by the dwell period and the dwellbandwidth; and if a narrowband pulse is present in the RF spectrum,determining from the energy map a pulse bandwidth and pulse duration ofthe pulse.
 2. The method of claim 1, wherein the collected RF energyincludes energy from a communications signal and energy from a radarsignal.
 3. The method of claim 1, wherein the collected RF energyincludes energy from a transient signal and energy from a continuoussignal.
 4. The method of claim 1, further comprising: determining fromthe energy map whether noise is present in the RF spectrum; and if noiseis present in the RF spectrum, filtering the noise from the energy map.5. The method of claim 1, further comprising: if a pulse is present inthe RF spectrum, generating a tag that is associated with the pulse andincludes a pulse characterization parameter that characterizes thepulse.
 6. The method of claim 5, wherein the pulse characterizationparameter is based on pulse width, center frequency, angle of arrival,or time of arrival.
 7. The method of claim 1, further comprising:determining, for a first pulse and a second pulse, a combined pulseduration that extends from a start time of the first pulse to an endtime of the second pulse; determining whether the start time of thesecond pulse exceeds the end time of the first pulse by less than apredefined threshold; and if the start time of the second pulse exceedsthe end time of the first pulse by less than the predefined threshold,combining the first pulse and the second pulse.
 8. The method of claim7, wherein the predefined threshold is based on the combined pulseduration.
 9. A method for processing RF signals comprising: receiving aset of time domain energy samples representing signal energy present ina wideband RF spectrum; transforming the set of time domain energysamples into a set of frequency domain power samples; determining fromthe set of frequency domain power samples whether a narrowband signal ofinterest is present in the RF spectrum; and forwarding to a follow onsystem a subset of the set of frequency domain power samples, whereinthe subset corresponds to the signal of interest.
 10. The method ofclaim 9, wherein transforming the time domain samples comprises:dividing the set of time domain energy samples into a plurality of Nwindows, each of which is associated with a predefined window period;and performing an FFT on each said window to generate a set of Kfrequency bins, wherein each said frequency bin has a value based onenergy present in a predefined frequency band during the correspondingwindow period.
 11. The method of claim 9, wherein determining whetherthe signal is present comprises: generating an energy map thatrepresents energy present in the RF spectrum as a function of frequencyand time.
 12. The method of claim 11, wherein the energy map is a bitmapcomprising N×K frequency cells, wherein each said frequency cell has abinary value based on the value of a corresponding frequency bin. 13.The method of claim 12, wherein the binary value is based on whether thevalue of the corresponding frequency bin exceeds a predefined threshold.